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Article

Hyperspectral Pansharpening Based on Homomorphic Filtering and Weighted Tensor Matrix

by 1,2, 1,2,*, 3, 2 and 1,2
1
Joint Laboratory of High Speed Multi-Source Image Coding and Processing, School of Telecommunications Engineering, Xidian University, Xi’an 710071, China
2
State Key Lab. of Integrated Service Networks, School of Telecommunications Engineering, Xidian University, Xi’an 710071, China
3
Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USA
*
Author to whom correspondence should be addressed.
Received: 5 February 2019 / Revised: 22 April 2019 / Accepted: 23 April 2019 / Published: 27 April 2019
Hyperspectral pansharpening is an effective technique to obtain a high spatial resolution hyperspectral (HS) image. In this paper, a new hyperspectral pansharpening algorithm based on homomorphic filtering and weighted tensor matrix (HFWT) is proposed. In the proposed HFWT method, open-closing morphological operation is utilized to remove the noise of the HS image, and homomorphic filtering is introduced to extract the spatial details of each band in the denoised HS image. More importantly, a weighted root mean squared error-based method is proposed to obtain the total spatial information of the HS image, and an optimized weighted tensor matrix based strategy is presented to integrate spatial information of the HS image with spatial information of the panchromatic (PAN) image. With the appropriate integrated spatial details injection, the fused HS image is generated by constructing the suitable gain matrix. Experimental results over both simulated and real datasets demonstrate that the proposed HFWT method effectively generates the fused HS image with high spatial resolution while maintaining the spectral information of the original low spatial resolution HS image. View Full-Text
Keywords: Hyperspectral pansharpening; homomorphic filtering; weighted tensor matrix; hyperspectral image; open-closing morphological Hyperspectral pansharpening; homomorphic filtering; weighted tensor matrix; hyperspectral image; open-closing morphological
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MDPI and ACS Style

Qu, J.; Li, Y.; Du, Q.; Dong, W.; Xi, B. Hyperspectral Pansharpening Based on Homomorphic Filtering and Weighted Tensor Matrix. Remote Sens. 2019, 11, 1005. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11091005

AMA Style

Qu J, Li Y, Du Q, Dong W, Xi B. Hyperspectral Pansharpening Based on Homomorphic Filtering and Weighted Tensor Matrix. Remote Sensing. 2019; 11(9):1005. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11091005

Chicago/Turabian Style

Qu, Jiahui, Yunsong Li, Qian Du, Wenqian Dong, and Bobo Xi. 2019. "Hyperspectral Pansharpening Based on Homomorphic Filtering and Weighted Tensor Matrix" Remote Sensing 11, no. 9: 1005. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11091005

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